Spectral domain optical coherence tomography (SDOCT) has been used for assessing structural changes associated with retinal degenerative diseases. However, while SDOCT can resolve histological layers, it lacks the resolution to measure cellular features. In recent SDOCT studies, only a modest correlation between the thickness of the retinal nerve fiber layer and the progression of glaucoma has been shown, with similar findings seen between the thickness of the photoreceptor layer and progression of retinitis pigmentosa. This reflects the fact that thickness of an individual retina layer does not necessarily correlate with the health or anatomical condition of its constituent cells.
Direct imaging of cellular features can allow detection of degenerative conditions based on changes in cellular structure or composition. However, practical retinal imaging systems, such as SDOCT and scanning laser ophthalmoscopy (SLO) achieve a factor of 5-10 fold lower resolution than is required for cellular resolution because imperfections in the human eye lens introduce random phase errors in transmitted light that blur out fine image details. A complementary technology, adaptive optics (AO), uses wavefront sensing techniques to measure aberrations caused by imperfections in the eye. This information may then be used as input to a correction approach, such as a deformable mirror, to correct for these aberrations.
However, the use of AO comes at a very large increase in system cost and complexity. AO imaging of awake human patients requires real time wavefront sensing and correction with subwavelength accuracy on a millisecond time scale in response to patient movement. This represents a formidable engineering challenge. From the clinical perspective, the high cost of AO is likely to preclude its widespread use in screening patients for retinal disease. Consequently, there is a significant need for improved systems and techniques for retrieving information about the health and organization of retinal layers and similar sample features.